With population growth and the acceleration of urbanization, fires have always been a safety problem. The rapid development and popularization of smart fire-fighting Internet of Things technology, communications, and artificial intelligence technologies have given traditional fire-fighting equipment the ability to perceive and connect to the network, thereby obtaining a large amount of multi-dimensional fire-fighting data, which can be used to train intelligence based on the data. The machine vision analysis model of fire protection improves the accuracy and efficiency of the recognition of smoke and flame.

Through the transformation of traditional fire-fighting equipment such as fire hydrants, fire water tanks, sprinkler equipment, surveillance cameras, etc., it not only has the existing fire-fighting functions, but also becomes a sensor for acquiring key fire-fighting monitoring scene data, providing real-time data for the fire-fighting cloud platform Or non-real-time data, through the labeling, mining and analysis of the data, the fire recognition algorithm model is continuously trained, so that the algorithm can not only quickly detect the fire and give an alarm, but also judge whether it is by distinguishing different smoke and flame colors. If there is a flame caused by a combustion aid, any gas or fuel, the key is to call the police and put out the fire as soon as the fire occurs.

Smart Fire Internet of Things

The focus of the construction of the Internet of Fire Fighting is fire early warning. According to the existing fire fighting experience, it is known to build an automatic warning system suitable for densely populated areas or areas with high fire occurrences. With the assistance of mobile communication networks, it can provide early warning of safety risks and promptly send out fire escape information. , Can greatly shorten the time required for evacuation. Specifically, information technology should be used to develop an automatic early warning system to monitor the operation of firefighting devices in real time. Then, based on historical fire data, cluster analysis is used to determine the characteristics of temporal and spatial distribution, and compare predictions with statistical data. To determine the impact of the fire, and to carry out rescue work based on the law of the fire in time and space to improve rescue efficiency.

Promote the construction of the fire-fighting Internet of Things system, introduce supporting facilities, and improve the level of systematization of dispatching. For the fire safety system to work efficiently, it requires close cooperation between various software and hardware systems. Therefore, we have to introduce supporting facilities to ensure that different facilities can work normally and well, and to improve the stability of the system, so that timely and effective dispatch can be carried out to ensure the safety of people's property in the fire fighting area.

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